Springer, 2019. — 105 p.Data-intensive systems are a technological building block supporting Big Data and Data Science applications. This book familiarizes readers with core concepts that they should be aware of before continuing with independent work and the more advanced technical reference literature that dominates the current landscape.The material in the book is structured following a problem-based approach. This means that the content in the chapters is focused on developing solutions to simplified, but still realistic problems using data-intensive technologies and approaches. The reader follows one reference scenario through the whole book, that uses an open Apache dataset.The goal of this book is to take the reader off the ground quickly and then consistently deepen understanding based on a driving example. It would usually require jumping between content in several different books covering various technologies. The focus is on learning a new way of thinking and an approach necessary for big data processing, rather than specifics of technologies that are changing fast. This way the reader gets a solid basis for further steps in the field.Preface Introduction Hadoop 101 and Reference Scenario Functional Abstraction Introduction to MapReduce Hadoop Architecture MapReduce Algorithms and Patterns NOSQL Databases Spark
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